Observation platform using structured communications for generating, reporting and creating a shared employee performance library

ABSTRACT

An observation platform determines relative responsiveness of enterprise employees to requests by external systems. A computer system of the observation platform monitors responses and actions of users to requests from external systems from a plurality of communication devices routed through a radio access point associated with the computer system. Each of the communication devices is associated with a user who is an employee in an enterprise. Information is extracted from the communications including users&#39; identities. Performance of a plurality of the users is measured based on aspects of the extracted information related to the relative responsiveness to requests from the external systems. The requests are posed by the external systems and responded to via the communications devices. A numerical ranking of the measured performance by each of the plurality of measured users, with respect to others of the plurality of measured users, at responding to the requests is assigned.

CROSS-REFERENCE TO RELATED U.S. APPLICATIONS—CONTINUATION

This application is a continuation of and claims the benefit of patentapplication Ser. No. 16/195,555, now U.S. Pat. No. 10,558,938, entitled“OBSERVATION PLATFORM USING STRUCTURED COMMUNICATIONS FOR GATHERING ANDREPORTING EMPLOYEE PERFORMANCE INFORMATION,” by Christopher N. Todd etal., with filing date Nov. 19, 2018, which is herein incorporated byreference in its entirety.

U.S. Pat. No. 10,558,938 is a continuation of and claims the benefit ofthe then patent application Ser. No. 15/470,235, now U.S. Pat. No.10,134,001, entitled “OBSERVATION PLATFORM USING STRUCTUREDCOMMUNICATIONS FOR GATHERING AND REPORTING EMPLOYEE PERFORMANCEINFORMATION,” by Christopher N. Todd et al., with filing date Mar. 27,2017, which is herein incorporated by reference in its entirety.

U.S. Pat. No. 10,134,001 claims priority to the then provisional patentapplication, Ser. No. 62/314,106, entitled “OBSERVATION PLATFORM USINGSTRUCTURED COMMUNICATIONS FOR GENERATING, REPORTING AND CREATING ASHARED EMPLOYEE PERFORMANCE LIBRARY,” with filing date Mar. 28, 2016,which is herein incorporated by reference in its entirety.

U.S. Pat. No. 10,134,001 is a continuation-in-part application of andclaims the benefit of the then patent application Ser. No. 14/869,167now U.S. Pat. No. 10,069,781, entitled “OBSERVATION PLATFORM USINGSTRUCTURED COMMUNICATIONS WITH EXTERNAL DEVICES AND SYSTEMS,” withfiling date Sep. 29, 2015, which is herein incorporated by reference inits entirety.

U.S. Pat. No. 10,134,001 is a continuation-in-part application of andclaims the benefit of then patent application Ser. No. 15/375,725, nowU.S. Pat. No. 9,928,529, entitled “OBSERVATION PLATFORM FOR PERFORMINGSTRUCTURED COMMUNICATIONS,” with filing date Dec. 12, 2016, which isherein incorporated by reference in its entirety.

U.S. Pat. No. 9,928,529 is a continuation application of and claims thebenefit of then patent application Ser. No. 14/320,356, now U.S. Pat.No. 9,542,695, entitled “OBSERVATION PLATFORM FOR PERFORMING STRUCTUREDCOMMUNICATIONS,” with filing date Jun. 30, 2014, which is hereinincorporated by reference in its entirety.

U.S. Pat. No. 9,542,695 is a continuation-in-part application of andclaims the benefit of then patent application Ser. No. 13/401,146, nowU.S. Pat. No. 8,948,730, entitled “OBSERVATION PLATFORM FOR USINGSTRUCTURED COMMUNICATIONS,” with filing date Feb. 21, 2012, which isherein incorporated by reference in its entirety.

U.S. Pat. No. 8,948,730 claims priority to the provisional patentapplication, Ser. No. 61/445,504, entitled “ENABLING A RETAILSALES/SERVICE PROVIDER TO INTERACT WITH ON-PREMISE CUSTOMERS,” withfiling date Feb. 22, 2011, which is herein incorporated by reference inits entirety.

U.S. Pat. No. 8,948,730 also claims priority to the provisional patentapplication, Ser. No. 61/487,432, entitled “ACTIVITY COORDINATINGASSOCIATE'S AUTOMATIC SERVICE ASSISTANT,” with filing date May 18, 2011,which is herein incorporated by reference in its entirety.

U.S. Pat. No. 9,542,695 is a continuation-in-part application of andclaims the benefit of then patent application Ser. No. 13/665,527, nowabandoned, entitled “AUDIBLE COMMUNICATIONS FOR QUERIES WITH INFORMATIONINDICATIVE OF GEOGRAPHIC POSITION,” with filing date Oct. 31, 2012,which is herein incorporated by reference in its entirety.

U.S. Pat. No. 9,928,529 is also a continuation-in-part application ofand claims the benefit of patent application Ser. No. 13/665,527, nowabandoned, entitled “OBSERVATION PLATFORM FOR PERFORMING STRUCTUREDCOMMUNICATIONS,” with filing date Oct. 31, 2012.

BACKGROUND

Modern communication devices provide for many communication and businessanalytics opportunities in retail, hospitality, industrial, enterpriseand other settings. Many communication devices have multiple functionsand have wireless connectivity options. Additionally, many differenttypes of devices, systems, and/or objects may be networked togetherincluding devices with electronics, software, sensors, beacons,smartphones and Internet-of-Things (IoT) devices; thereby enabling datato be exchanged between devices and statistically gathered over thenetwork. Given the present communications devices and the presentnetwork connectivity, the data gathered can be quantified, organized,and analyzed to represent an objective, numerical, performance profilefor an individual user of the system.

The present technology describes how an observation platform can be usedto integrate multiple devices or external systems, and how informationthat traverses the observation platform can contain valuable statisticalinformation which can be manipulated, analyzed and classified throughcomputer algorithms using the context of users and devices, combinedwith the policy of the enterprise or the default observation platformpolicy, to yield higher level metrics that indicate the efficiency,effectiveness and performance of individuals engaged in the use of theobservation platform.

BRIEF DESCRIPTION OF THE DRAWINGS AND TABLES

FIG. 1 is a flowchart of an overview of the observation platform, inaccordance with an embodiment.

FIG. 2 is a flow diagram illustrating the connections betweenobservation platforms and to external devices, IoT, beacons and externalsystems, in accordance with one embodiment.

DESCRIPTION OF EMBODIMENTS

Reference will now be made in detail to embodiments of the presenttechnology, examples of which are illustrated in the accompanyingdrawings. While the technology will be described in conjunction withvarious embodiments, it will be understood that they are not intended tolimit the present technology to these embodiments. On the contrary, thepresent technology is intended to cover alternatives, modifications andequivalents, which may be included within the spirit and scope of thevarious embodiments as defined by the appended claims.

Furthermore, in the following description of embodiments, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present technology. However, the present technologymay be practiced without these specific details. In other instances,well known methods, procedures, components, and circuits have not beendescribed in detail as not to unnecessarily obscure aspects of thepresent embodiments.

Unless specifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the present descriptionof embodiments, discussions utilizing terms such as “receiving,”“recognizing,” “deriving,” “storing,” “relaying,” “executing,”“generating,” “determining,” “tracking,” “recording,” “identifying,”“locating,” “making,” “delivering,” “scheduling,” “specifying,”“translating,” or the like, refer to the actions and processes of acomputer system, or similar electronic computing device. The computersystem or similar electronic computing device, such as a wearablecomputer, telephone, smart phone, tablet computer, handheld mobiledevice, or other connected IoT device manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission, or display devices.Embodiments of the present technology are also well suited to the use ofother computer system technologies such as, for example, optical,quantum and mechanical computers.

A user or users, as referred to herein, may be a person or people suchas, sales associates, employees, managers, trainees, trainers, doctors,clinicians, patients, customers, emergency responders, personnel, etc.In one embodiment, the user interfaces with a device for communicationswith other users or interaction with external systems. Such a device maybe a handheld device, a headset, a smartphone, a tablet, an earpiece, aradio, a computer system, or other device capable of providingcommunications across the network. Such users may be part of theenterprise operating the observation platform or they may be external tothe operating entity (e.g., customers, shoppers or visitors) and desireaccess to users, information or control of devices within the attacheddata network.

Customers or visitors may refer to anyone within the environment of anobservation platform who are not directly connected to the observationplatform system but may do so by using the wearable devices or otherapplications (apps) designed to connect by permission and arrangementdirectly with the observation platform. In one embodiment, customers orvisitors may refer to individuals who are purchasing or shopping foritems or services in store or hospitality environment, past customers,potential customers, perspective customers, shoppers, browsers, orothers who enter the store environment as a potential client and notwith the same operational access an employee does.

Overview of Observation Platform Using Structured Communications withExternal Devices and Systems

Overview of the Observation Platform

The present technology employs an observation platform for structuredcommunications and for gathering or collecting statistics about theusers, devices and external systems that may connect to the observationplatform. The following overview describes how an observation platformis used for communications between devices and computer systems and isused for collecting statistics from devices, communications and computersystems. Individual details, embodiments, and components of theobservation platform described herein may or may not be used forembodiments pertaining to using observation platform to measure andquantify user performance scores, grades, benchmarks or rankings.

Using structured communications in an observation platform, as referredto herein, may refer to the following actions regarding communicationsbetween two or more users or one user and one or more computer systems:mediating, disciplining, structuring, controlling, participating,discouraging, encouraging, influencing, nudging, making an example of,permitting, limiting, managing, validating for compliance with policies,measuring what goes on as a communication occurs, characterizing,enabling, observing, recording, correcting, directing, informing,requesting, alerting, etc.

Embodiments of the present technology are for an observation platformusing structured communications including communication with externaldevices and systems. An observation platform may involve a number ofusers and provides structured and disciplined communications for theusers and associated devices as well as captures data regarding thecommunications such as user context and performance metrics. The presenttechnology may be employed in various environments such as retailsettings, public-stage floors, outdoor venues, concerts, policescenarios, disaster areas, schools, sporting events, hospitalityoperations, hospitals, clinics, security operations, militaryoperations, a prison organization, customer service centers, callcenters, manufacturing organizations, a factory floor, warehouseoperations and other environments where humans work together, withexternal computer systems or other IoT devices and where communicationsoccur between users and/or the external systems or devices.

The mediating, structuring or disciplining process envisioned hereininvolves using a communications and computer system as a platform toreceive communications or instructions from users, devices, externaldevices, IoT devices and computer systems; to generate or parse metadatarelated to the communication or instruction; and to relay, forward,delay or inhibit the communication or instruction based on the context(metadata) of each user, device or system.

Communications are enabled by multiple means including: simply speakingcommands, saying names, asking questions, issuing commands,press-to-talk broadcasting to groups or segments of the observationplatform, talking to specific groups or locations; listening topre-established information (e.g., podcasts, messages, announcements,huddles, chat-ins or enterprise related info-casts), by users movinginto locations where information is presented or requested based onlocation and context of the user or proximal device, listening toinformation from other users on the system either real-time or delayed,listening to information from other systems or processes within thissystem related to actions required or information necessary to work moreefficiently.

The system, including other users, may prompt users for verbalcontributions to the data store or seek selected button presses todetermine further context or supplemental information and actions of theuser. In conjunction with the communications and signaling informationbeing exchanged, the system collects other relevant data which mayinclude signals or sounds useful for location determination; soundsuseful for system optimization; sounds useful for user identification,role determination, or environment detection; or other signalinformation (audible, sub-audible or optical) relevant to operating inthe desired environment.

With reference now to FIG. 1, a flowchart of an overview of theobservation platform is shown in accordance with an embodiment.

Environment 100 describes a physical location of a basic observationplatform. Within environment 100 are communication devices 205, 210, 215which represent one or a plethora of communication devices. Thesecommunication devices communicate using wireless signals connecting toRadio Access Points within the Environment 100. These communicationdevices may be owned by the enterprise, owned by the user, or owned bythe observation platform provider and may include, for example, asmartphone, a tablet computer, or a wearable device.

Devices 205, 210 and 215 may be user devices that are mobile andemployed by a user to communicate with other users via other devices.Communications between the devices may be described as signals. Thedevices 205, 210 and 215 may be a smartphone, a personal digitalassistant, a fob, a handheld device, a headset device or other smallelectronic device. In one embodiment, devices 205, 210 and 215 employspeakers and microphones with control buttons for audiblecommunications. The control buttons may be press to signal buttons, pushto talk buttons, volume control buttons, and power on/off buttons orother standard buttons and may be options on a touchscreen. Devices 205,210 and 215 may be handheld, may be worn around the neck, and may be aheadset worn on the head or behind the ear or otherwise interface withthe human body. Devices 205, 210 and 215 may or may not comprise ascreen or display such as a liquid crystal display (LCD). In oneembodiment, devices 205, 210 and 215 do not comprise a display such thata user is not inundated with too many options or too much informationfrom the device. A user device without a display may simplifycommunications and thus allow heads-up awareness and presence in theenvironment. Another user, such as a customer, may be more likely toemploy the device for its intended purpose if the human interface issimplified.

Devices 205, 210 and 215 and other devices in environment 100 may bedispensed to a user upon entering environment 100 or may be brought bythe user into environment 100. For example, in a retail settingassociates may be issued devices by the employer or owner of theretailer setting. Customers in the retail setting may also be issueddevices as they enter the retail setting. Customers may choose whetheror not to accept the device or whether or not to use the device afteraccepting it. The associate devices and the customer devices may or maynot be the same type or model of devices. Alternatively, the customermay bring a device into the retail setting such as a smartphone. Thecustomer may download an app to the smartphone that will allow thecustomer to use the device for communications in the store withassociates or others in accordance with present technology. The customermay remain anonymous or may elect to identify themselves. In oneembodiment, recognition of the customer's identity is not required foradditional services or offers.

There may be multiple radio access points (shown for example as 120 and130) and access point technology may include Wi-Fi, Bluetooth, privateradio or other wireless connections. An environment and may contain asingle or a plethora of radio access points as shown. In one instance,computer system 140 exists within Environment 100, and by using standardcomputer networking technologies such as switches, bridges, routers,firewalls, gateways, etc., the radio access points communicate with eachother and with computer system 140. Computer system 140 communicates viastandard networking technologies, possibly including the Internet 118,to the central control and database 400 using bi-directional Path A.Computer system 140 is optional in the physical location of environment100.

If the computer system 140 is not included in environment 100, Path B isused to communicate between the radio access points (120, 215, etc.) andan external computer system 150, which may reside in the cloud, in theenterprise, or at the service provider for the observation platform.Environment 100 must either contain computer system 140 or be connectedto computer system 150 in order to operate. It is possible to configurean observation platform with both computer system 140 and computersystem 150 connected for purposes of redundancy.

Access points 120, 130 and devices 205, 210, 215 employ standardtechniques for communicating wirelessly. The communications may beperformed using radio techniques such as near field communications,short wave radio, infrared, Bluetooth, Wi-Fi, standard wireless computernetwork protocols, etc. Devices 205, 210 and 215 may be able tocommunicate with each other directly or through access points 120 and130. Devices 205, 210 and 215 communicate with each other via thecomputer system 140 or 150. In one embodiment, all communications inenvironment 100 are relayed through the access points which act as acentral hub. For example, device 205 may communicate with device 210 bydevice 205 sending a communication to access point 120, computer system140 derives that device 210 is the destination for the communication andrelays the communication to device 210. This may occur automatically andquickly enough such that the users will not experience any undue lag incommunications. In one embodiment, devices 205, 210 and 215 maycommunicate directly with computer 140. For example, a user may issue acommand to computer 140 via device 205 or computer 140 may sendinformation to device 205. Information send from computer 140 to device205 may be an audible voice signal or may be textual, contextual,geographical or graphical data to be displayed at device 205 if it isproperly equipped to do so.

Computer systems 140 or 150 are responsible for the structuring ofcommunication and collection of analytical data from the devices (205,210, 215, etc.). Computer system 140 or computer system 150 report datato the central control and database 400 using either Path A or Path C asindicated. Additionally, the central control and database 400 providesinformation, instructions, messages, configuration data and policyinformation to computer system 140 or computer system 150 forstructuring and controlling the communication flow to and among theDevices within Environment 100. Central control and database 400contains the instructions for policy distribution to computer system 140or computer system 150. Central control and database 400 accumulates theprimary statistics and processes the algorithms for generating thesecondary statistics and higher-order statistics used to determinesystem health and user performance.

The central control and database 400 may reside with the provider of theobservation platform, in the cloud, within the enterprise or in acommercially available server farm facility. Central control anddatabase 400 provides one or more application programming interfaces(APIs) to any external systems 500 and information sources that maycommunicate with the users and devices within environment 100. Thecentral control and database 400 also provides encrypted and securestorage and access to computer systems 140 or 150.

External systems 500 may refer to external computer systems owned by theenterprise, third-party services, vendors or suppliers, or the providerof the observation platform. These external systems may containinformation that is pushed to users of the observation platform such astime-clock alerts, video analysis alerts, cross-channel or omni-channelsupport alerts, enterprise-wide or group-targeted messaging text oraudio, automated status alerts or other information that would be usefulto one or more users of the observation platform(s).

With reference now to FIG. 2, a flow diagram illustrating theconnections between observation platforms and to external devices,internet of things (IoT), beacons and external systems is shown inaccordance with one embodiment.

FIG. 2 uses the basic observation platform described in FIG. 1 andextends the overall system by: adding interconnections between otherobservation platforms; including within the observation platform a newenvironment 300 that contains one or more systems or alternate devices(series 300) that may interact with the devices (series 200) and henceusers of the system; and attaching to the observation platform a newenvironment 350 that contains one or more systems or alternate devices(series 300) that may interact with the user devices (series 200) andhence users of the system.

Interconnection of observation platforms allows information to beexchanged and structured across any set of locations, buildings, citiesor countries. The observation platforms, depicted in environments 100and 110, are usually connected under the control of computer system 150via Path A and Path B. Computer system 150 mediates the communicationbetween the sites, applies policy to the transiting information, andextracts statistical information for application to Secondary orHigher-order statistics used for performance evaluation of users or thesystem integrity.

Environment 110 shows in greater detail how other devices within theobservation platform environment can interact with user devices 205, 210and 215, the radio access points 120 and 130, and the computer system150 or 160.

Beacon devices 610, 615 and 620 are typically small fixed or mobiledevices that emit a signal to alert other devices that they are within anear proximity of the beacon. The beacon signal may containidentification information or other information useful to the receivingdevice. For example, visitor device 700 may detect beacon 620 whichcauses visitor device 700 to contact a separate system to receivespecial offers or information regarding the local proximity. Userdevices 205, 210 and 215 may be capable of receiving the beacon signalwhich may be used for proximity detection and location determination.Additionally, user devices 205, 210 and 215 may be capable oftransmitting a beacon signal to trigger actions in devices carried byvisitors or shoppers.

For example, visitor device 700 may detect a beacon signal from device215 which causes visitor device 700 to contact a separate system that inturn alerts the user of device 215 that they are in proximity to aspecific visitor or shopper along with information about that visitor orshopper. The observation platform might then communicate with anexternal system (e.g., external system 315) to receive instructions andpotential offers for that visitor or shopper.

Environment 300 depicts a set of other possible devices that may resideeither within the observation platform as shown in environment 110, orexternal to the observation platform environment as shown withenvironment 100 which is connected with path C.

In one embodiment, content distribution manager browser 300 (CDM) is asoftware application that is accessed via a uniform resource locator(URL) by any computing device that employs a web browser. Contentdistribution manager browser 300 may comprise an application programinterface (API) or graphical interface that is employed by a user ofcontent distribution manager browser 300. A user of content distributionmanager browser 300 may be required to provide authentication to accesscontent distribution manager browser 300.

Content distribution manager browser 300 is employed by a user to manageand control messages that are sent to a plurality of observationplatform and the devices therein. In one embodiment, contentdistribution manager browser 300 can retrieve content for a message orcan be employed to generate new and original content for a message. Inone embodiment, the content is an audio file such as a WAV file that isthe recording of an audible voice such that when the message isdelivered to and accessed by a destination device, the message willplayback the audible voice. The content distribution manager browser 300may be employed by a manager to record a voice message which is thendelivered to a plurality of devices.

In one embodiment, a message controlled by content distribution managerbrowser 300 is delivered to a plurality of devices simultaneously. Thismay be accomplished by content distribution manager browser 300 sendingout the message to the various devices at the same time, or contentdistribution manager browser 300 may deliver the message to a pluralityof observation platforms with commands or instructions to deliver themessage to specified devices within the observation platform at adesignated time. Delivering the messages to the devices may also bedescribed as pushing the message.

The manager using the content distribution manager browser 300 maydesignate the time a message should be available for the users and howlong that message should be available to hear (end time). Alternatively,the content distribution manager browser 300 may be employed to deliverthe same message to different devices at different times. For example,the message may be delivered to store managers within a set ofobservation platforms at a designated time before the message isdelivered to other employees within the same set of observationplatforms. The user of content distribution manager browser 300 may alsospecify that additional content or messages are sent to differentdevices. For example, additional content may be sent to store managersor additional content may be sent to devices associated with a specificdepartment in a retail setting such as the painting department.

In one embodiment, content distribution manager browser 300 is employedto specify who or what devices are to receive the message with itscontent. For example, the user of content distribution manager browser300 may have authority over several different environments each with itsown observation platform. The user may wish that the message only besent to specified observation platforms within the plurality ofobservation platforms. Alternatively, the user may specify that all ofthe devices within all of the observation platforms receive the message,or only devices located within the physical boundaries of theobservation platform at the designated time receive the message, or onlydevices associated with a specific department receive the message oronly devices associated with store employees and not customers receivethe message. The possible options for specifying which devices receive amessage and when are limitless.

A message may also be generated and sent to a specific individual. Inone embodiment, content distribution manager browser 300 uses groupingsby role, department, district, and/or region to determine which devicesplay a given message. It should be appreciated that the content of themessage may be a voice recording, but may also be other content such astext, images, or video. In one embodiment, the message is sent to agiven device with a command to notify the user of the device that thereis a message received. The notification may be a light, a blinkinglight, a specific color of light, a sound, a textual notification, orany other type of notification that the device is capable of providing.

Content distribution manager browser 300 may be employed by a user thathas high level access to the plurality of observation platforms. Forexample, a corporation may have hundreds or thousands of hospitalitylocations or store fronts that each makes use of an observationplatform. The corporation may have a headquarters or central office withemployees who have access to content distribution manager browser 300with the ability and authority to send a message to anyone and everyoneassociated with the corporation.

In one embodiment, a device that receives a message from contentdistribution manager browser 300 automatically sends a confirmation backto content distribution manager browser 300 that the message has beenreceived. Additionally, once the message has been accessed or heard bythe user of the device, the device may send a message back to contentdistribution manager browser 300 that the messaged has been heard orotherwise accessed. In one embodiment, the message may be a mandatorymessage that the user of the device is required to access a, listen toand then acknowledge hearing. For example, process 1200 herein describesvarious embodiments of mandatory messages and consequences, rules orpolicies associated with mandatory messages.

Another possible element of environment 300 is the manager application,305. In one embodiment, manager application 305 a software applicationor app that is accessed via a mobile computer system such as asmartphone or tablet. In one embodiment, the mobile computer systemexecutes an Android operating system. In one embodiment, the mobilecomputer system executes an iOS operating system. Other operatingsystems may also be employed. Manager application 305 may be an appavailable for download and installation on the mobile computer system.

The manager application 305 is designed with an API or graphicalinterface specific to a mobile computer system such as a smart phone andto be used in the field by a user or manager associated with at leastone observation platform. The user of manager application 305 may be aregional manager that has access to a plurality of observationplatforms. The regional manager may regularly travel between thephysical locations of the plurality of observation platforms and needsto have access to the observation platforms while physically remote andin the field on the go.

In one embodiment, manager application 305 allows the user of managerapplication 305 to communicate with or monitor any device or pluralityof devices within any of the observation platforms associated with theuser. Manager application 305 also is able to report statistics orobserve or monitor communications with any of the observation platformsassociated with the user. For example, the manager application 305 maybe able to listen in to communications happening in real time within anobservation platform or may be able to play back past recordedcommunications.

In one embodiment, the manager application may operate in a manneridentical to the mobile devices in the observation platform as apeer-like device. In this mode the manager application may broadcast ordirect communications to specific devices, receive alerts and provideboth a primary signal for communication and a secondary signal fordetermining geographic location. In one embodiment, the peer-like devicemay be able to operate and interact with devices within an observationplatform without directly communicating with a central computer system.In other words, the central computer system may or may not be requiredfor receiving and relaying messages from the manager application.

The manager application 305 may also be employed to send announcementsor messages similar to content distribution manager browser 300. Themanager application 305 may communicate directly through a network witha given observation platform or may use the external communications pathD, path A and path B.

Another possible element of environment 300 is an IoT device. Anobservation platform may be associated with external devices including agrowing list of electronic devices that communicate with each other orwith one or more systems providing status, alerts and other usefulcommand/control information. These external devices are able to operatein an observation platform and communicate using formatted data stringsand standard internet protocols for communication across the Internet.These external devices may be referred to as the IoT.

Specifically, external devices and systems 310 depicts a wide array ofIoT devices that can use the observation platform for alerting selectedusers of actions needed or that may query selected users for additionalinformation or may query the observation platform for user contextualinformation or may allow users to instruct or control the IoT devicesbased on user context and policy. Environment 300 comprises componentsthat may or may not be used with different embodiments of the presenttechnology and should not be construed to limit the present technology.

The present technology provides for many examples of how structuringcommunications may be used in various environments for a variety ofpurposes. The following discussion will demonstrate various hardware,software, and firmware components that are used with and in computersystems and other user devices for structuring communications usingvarious embodiments of the present technology. Furthermore, the systems,platforms, and methods may include some, all, or none of the hardware,software, and firmware components discussed below.

One purpose of structuring or disciplining a communication is for usersto become more productive and more effective customer service associatesor sales people in a retail or hospitality setting. The presenttechnology may accomplish this goal by monitoring communications of theusers that occur via communication devices. The communications may bemonitored to derive context information from the communication such asthe name of the user; geographic location of a user; the state or statusof the user (e.g., busy, available, engaged, conversing, listening,out-of-range, not logged on, etc.); the user's interaction with others;proximity to other users, visitors, beacons, cellular phones or otherelectronic signals; commands or instructions from the user to externaldevices; instructions or requests from external devices to the user; howthe user responds, both physically and verbally, to those instructionsor requests.

An observation platform may be associated with external devicesincluding a growing list of electronic devices that communicate witheach other or with one or more systems providing status, alerts andother useful command/control information. These external devices areable to operate in an observation platform environment and communicateusing formatted data strings and protocols for communication using theApplication Programming Interface (API) for the observation platform.These external devices form a diverse group of equipment that may bereferred to as the IoT. Other devices within the observation platformmay use radio techniques such as Bluetooth or Bluetooth Low Energy (BLE)to signal their presence and information. These devices are sometimescalled “beacons” and may be used for triggering actions on thewearables, smartphones or other devices carried on a person.

In one embodiment, the structured communications of the observationplatform allows users to directly and immediately interact with externaldevices through the mechanisms of the observation platform such as:interpretation and recognition of spoken words (e.g., speech-to-textspeech recognition), geographic location determination provided by theobservation platform, proximity location provided by external cues(e.g., a beacon, environmental sounds, sub-audible sounds, RF signals oroptical trigger) and optional identification of end users, their roles,assigned tasks, primary locations and individual responsibilities.

The external devices may collect and gather data via sensors such ascameras, motion sensors, heat sensors, door counters, proximity sensors,beacons, temperature sensors, etc. The collected data may be exchangedvia a network such as the network associated with the observationplatform or via separate connections or networks. Moreover, theobservation platform may be able to make inferences from communicationsand locations within the observation platform as well as make inferencesfrom the data gathered by the external devices. The inferences may thenbe combined with policy and used to structure the communications and/orstructure the control or requests from the external devices or takeother actions within the observation platform.

An observation platform is used to connect enterprise users with eachother and connect the users with external devices and computer systemsassociated with the observation platform. Policy, as accepted ordetermined by the controlling enterprise, determines the mostappropriate action for commands to other users or external devices, orrequests from other users or external devices. The policy can useinformation such as, but not limited to: location, history, recognitionof spoken words and optional personal identification including roles andresponsibilities, plus any information provided by the external system,to dynamically determine and control how communications, commands,information or other requests and responses are routed through thesystem and presented to the users or external devices.

The users of the observation platform carry or wear devices capable ofcapturing the person's voice, surrounding information such as sounds andsub-audible sounds, electronic signals or optical signals. Sub-audiblesounds may or may not be made by a human user but are sounds that areout of the range of normal human hearing. Electronic signals may be anyradio (RF) device or beacon which emits a radio signal that is receivedby the user wearable device regardless whether the signal is demodulatedor understood by the user device. The wearable device develops a secondsignal from the surrounding information which is used by the observationplatform to determine the nature of the external device, the location ofthe device and indicative of the current context of the associated user.The device may be a commercially available device or, may be a specificpurpose device built for the present technology normally using a buttonthat is pressed to capture the user's voice when specific actions aredesired by the user.

The communications, motions and behaviors of users within theobservation platform may be monitored to derive context and userperformance information from the raw statistics gathered by the system.Many raw statistics are used to dynamically determine source and,separately, destination context by the system and are selected from thegroup raw statistics such as: engagement time; available times; locationmaps for the enterprise layout; listen time and who or what was listenedto; talking time on the system; non-system directed talking time asdetected by the wearable; number and role of listeners to messages sentto the system; urgency of listeners to hear a message from the user;geographic location of devices and users; locations traversed includingspeed and direction; time in-zone and not-in-zone; who initiatedcommunications to which individuals, groupings of individuals orexternal systems; type of communication (e.g., broadcast, privateindividual, private group, announcement, voice-to-machine,machine-to-user, initiate requests for actions, respond to requests foraction etc.); length of communication; location of initiatingcommunications; locations of receiving communications; intonation;speech pacing; lengths of speeches; lengths of speech segments; when,where, and for how long two or more users dwell in close proximity toeach other or to specific locations; speed of movement and pausing oflistening individuals during/after talking or listening to another useror a connected system; frequency that listeners delay hearing a message,drop out from what a speaker is saying, or delete a message beforehearing it from a user; questions asked and how long the question takesto ask; questions answered, and how long it takes to respond and thenanswer the question; action requests initiated from users or externalsystems, action requests responded to from users or external systems,from where, to where and any associated motions; promptness of responsesto what was heard from users or from external systems, button pressesand button press durations as initiated by the user; which policies areinvoked by the observation platform for the communications between usersor external systems.

Context determination is the result of the analysis of the rawstatistics within the observation platform and using the default orexplicit policy of the enterprise to determine the most appropriaterecipient of information from either users or external devices, or whereand when to most appropriately deliver messages, communications,commands, requests or instructions to users or devices.

The structuring or disciplining process described herein involves usinga communications and computer system as a platform to first, build acontext and contextual history of each user or device based on actions,motions, instructions, reactions and behaviors within the observationplatform, and second, use policy and inference to make determinationsfor distributing information, instructions or voice files; collectinginformation; generating requests for actions; accepting and playingrequests for action; generating or distributing instructions orindicators from users or external systems; or delivering information,requests, instructions, indicators, a user's voice, or other audiblemessages to other users or other external systems.

For example, the observation platform may listen to commands from users,interpret those commands, establish two-party and multi-partycommunications links, pass on messages, and store messages and commandsfor later action. Commands from users, internal rules and policies, orexternal systems may indicate the need for specific assistance atspecific locations and policy may determine how many and which usershear the request or which external systems are issued instructions. Theinterpretation of commands may also be relayed or forwarded to theexternal devices as instructions to that device or plurality of devices.

A response by a user or external system may be voluntary or mandatory,based on policy, and is processed by the observation platform usingcurrent context, historical context, inference and policy to determinethe most appropriate actions. Data is collected about each step of theprocess thereby permitting an owner or manager or a group of people toobserve and analyze the effectiveness of the group's interactions witheach other and with external systems. Aggregating the data and employinganalytical tools within the observation platform can result inperformance information for each user of the system, groups of users,wearable device and external systems.

The communications may be monitored by a computer connected to theobservation platform which is associated with a data network that actsas a conduit for the user communications, primary statistics, actionsand reactions of users and devices. The data network may consist of, butis not limited to: wireless connections (typically, but not exclusivelyWiFi), wired connections, optical connections, switches, routers,firewalls, encryption devices, private networks, Internet segments,cloud-based segments and enterprise-owned servers.

The computer system may convert audible, voice or speech communicationsto text or machine-compatible format using standard and well-knownspeech recognition techniques. Decoding the spoken words may determinecommands, identify keywords, and initiate actions based on the wordsspoken and the button that is pressed at the time of speaking. Thedecoded text in combination with location or proximity to other devicesmay be used to derive additional context information from thecommunication. The computer system may also store some or all of thecommunication including the audible portion of the communication, thecontext of the user, the decisions made by inference and policy, and thedecoded text of the communication.

The computer system may also be in communication with the externalsystems, external devices or IoT devices and have the ability to issueinstructions to the external devices or receive instructions from theexternal devices. Instructions may be requests for action, responses toinstructions or information that is useful to the user or the connectedsystem.

The structured communications may extend beyond a single venue tomultiple venues or enterprise locations without regard to geographicspacing. A plethora of observations platforms may be connected via anetwork and will interoperate according to the programmed policy.

In one embodiment, the computer system uses the context informationderived from the raw data combined with policy to determine adestination of the communication and forwards, delays, inhibits,modifies or relays the communication to the destination. For example, afirst user may attempt to contact a second user via communicationdevices. The first user sends the communication to the computer systemassociated with the observation platform using the network. The computersystem recognizes the first user's speech or instruction and is able toderive current context information to add to historical contextinformation for determining that the communication's destination is athird user. The computer system then relays the communication, via thenetwork, to a communication device associated with the third user. Thecomputer system may also convert the communication to text and deriveadditional instructions or performance metrics regarding the first orthird user. For example, the first user may be an associate in a retailsetting and the third user is a customer. The first user may beresponding to a query made by the third user. The performance metric maybe the length of time it took for the first user to respond to thequery, how long it took to respond to the query, whether or not thequery was satisfied, the geographic location of the first user, thegeographic location of the third user, the proximity between the users,or may be a different metric entirely. The computer system may deriveand store more than one performance metric for each communicationexchange.

In one embodiment, the computer system generates or parses metadatarelated to a communication and also knows metadata for each of aplurality of devices in the observation platform. The computer system isthen able to match an attribute of the metadata from the communicationto an attribute from the metadata of at least one of the plurality ofdevices in the observation platform. The communication may then beforwarded to the matched device(s). The metadata may be described asattributes, tags, or characteristics of a communication. Thecommunication may be a signal generated by user device and may comprisespeech, text, audio, video, button-press instructions, or a combinationthereof.

The attributes of the metadata may not be associated with the content ofthe signal and are related to context of the signal such as the time thesignal was sent, an identification of the device that sent the signal, alocation of the device when the signal sent, a geographic zone thedevice is located in, history of the device sending communication, etc.In one embodiment, the metadata is associated with the content of thesignal, such as text. The generating of metadata and relaying of thecommunication may occur substantially in real time such that a user ofthe device does not perceive any delay in communications.

In one embodiment, the computer system is able to determine geographiclocations of users based on information received from communicationdevices associated with the users. The geographic location data may bestored as data associated with a user's communications device at aparticular time, or as a performance metric, or may be combined withother information to generate a performance metric. The location data isan element of the context of the user or external device and is used bythe observation platform for structuring the flow of all information.Geographic information and user motion combined become elements of theuser context and are used with policy to determine the source ordestination of information processed by the observation platform.

Observation Platform Using Structured Communications for DeterminingPersonal Performance Metrics

Definitions

Primary Statistics: Those numerical observations that can be obtained bythe observation platform from direct use of any and all of itscommunication-mediating, location-sensing powers and context informationgathering. The primary statistical data may include, but not limited tosuch directly measurable quantities such as: engaged or availabletime(s), locations; locations traversed including speed and direction;listen time; talk time; number of listeners, geographic location of thespeakers and listeners; type of communication (e.g., broadcast, privateconversations with individuals, private conversations with groups,interruptions, group conversations, announcements, interruptingannouncements, mandatory response messages, voice-to-machine,machine-to-user, initiation of requests for action, responses torequests for action, etc.); number and role of listeners to messagessent to the system; length of the communication session(s); location ofsaid communications for initiating devices, users or external systems,participants in the form of devices or users or external systems,destination devices, users or external systems; instructions sent orreceived from external systems of communications; keywords spoken orlistened to; intonation; speech cadence, banter rate, emotion andinflection; lengths of speech segments; locations traversed by users ordevices including speed and direction; when, where, and for how long twoor more users or devices dwell in close proximity to other users of theobservation platform, or to visitors to the enterprise, or to specificlocations, or to other external devices such as beacons, IoT devices orkiosks; the speed of movement for talking or listening devices or usersduring/after talking or listening; time in specified zones and not inspecified zones; the frequency that listeners delay hearing a message ordrop out from what a speaker is saying, or delete a message beforehearing it from a user or device; number and type of questions asked andhow long the question takes to ask; number and type of questionsanswered, and how long it takes to initially respond and thencommunicate the answer to the question; number and type of actionrequests initiated from users or external systems and number and type ofaction requests responded to from users or external systems, from where,to where and associated motions; promptness of responses to what washeard from users or from external systems; promptness of responses towhat was heard; other audible or sub-audible sounds captured by thewearable; button presses and button press durations as initiated by theuser; inertial measurement unit (IMU) data; radio signal strength (RSS)data; signal to noise ratio (SNR) data; interfering signal data; batterycharge state data; temperature data; and (X,Y,Z) location data for allusers, devices or other systems capable of being tracked or reportinglocation information. Finally, the primary statistics will include anypolicies which are invoked by the observation platform for thecommunications between users or external systems. Time of day willnormally be included in each primary statistic. These items may also bereferred to as first order data and metrics, primary observation,primary metrics, or primary characteristics.

Subjective Statistics: Those numerical and text-based statisticsprovided through mechanisms such as: polling other users, visitors orthrough polling from external systems; surveying other users, visitorsor through surveying from external systems; getting survey-like feedbackfor a user following a contact or close proximity detection with avisitor as determined by the policy used by the observation platform;getting survey-like feedback for a user following a contact or closeproximity detection another user as determined by the policy used by theobservation platform; comments, notes and interpretations provideddirectly by the user; comments, notes and interpretations provided bymanagement regarding the user;

Sociability Statistics: Quantified metrics that conveys the ability andwillingness of a person to communicate, educate, or engage with others.May also be referred to as social engagement quotient (SEQ), socialquotient (SQ), social skills, people skills, social factors, or socialpotential. [See U.S. Pat. No. 9,053,449 entitled “Using StructuredCommunications to Quantify Social Skills, assigned to the assignee andincorporated by reference herein]

External Statistics: Those numerical observations that are useful ingenerating Secondary Statistics or higher-order statistics but that comefrom outside the scope of the observation platform. One example is thegross sales receipts from a cash register which might be tiedstatistically to circumstances visible to the observation platform viaan observed basket identification. A second example would be somemeasurement of shopper locations by an external smartphone application.These numerical statistics may be gathered by the external system whichthen reports the data to the observation platform; or may be gathered bythe observation platform working together with the external system tocoordinate user context and locations; or may be gathered by theobservation platform using data it requests from the external systems toderive the external statistic. May also be referred to as externalobservations.

Secondary Statistics: Those numbers generated through the application ofInference rules, combinatorial rules and policy controls and feedbackfrom the primary statistics, subjective statistics, sociabilitystatistics and the external statistics. These items may also be referredto as second order data and metrics, secondary observations, secondarymetrics, or secondary characteristics. Contextual information andhigher-order statistics may be derived from secondary statistics and maybe used for determination of message flow, device actions, and controlof all users and devices connected to the observation platform.Secondary statistics include data relative to user performance and tosystem and device performance.

Higher-order Statistics: Those numbers generated through the applicationof Inference rules or combinatorial rules from the primary statistics orsecondary statistics. Additionally, the higher-order statistics form thebasis for calculating a user's performance score using statisticalcategories such as, but not limited to: user performance score relativeto others in the same or similar environments, user performance scoresrelative to others with the same or similar rolls and responsibilities,user ranking against other users, user ranking against establishedperformance norms; the establishment or adjustment of performance normsand performance benchmarks for roles, responsibilities, environments,zones within environments, titles, geographic regions, enterprisefunctions and enterprise size. May also be referred to as higher-orderobservation or higher-order metrics.

Enterprise Performance Data: Goals and results, expressed as metrics ornumbers, that comprise both a specification of what is important intargeted performance and that also contain historical data which can beuseful in learning about what seems to matter, either through humaninsight or intuition, or through machine-learning via an algorithmicprocedure. May also be referred to as outcome data or desiredobjectives.

Inference rule: A specific algorithmic procedure for processing aspecified set of input data to produce a specified set of output data.At the simplest, the procedure may be to construct a weighted sum of theinputs to produce a single output number. May also be referred to ascombinatorial rule.

Validated Rules: Inference rules that have been proved to have a usefuland meaningful correlation to desired objectives by comparing the higherorder statistics that they generate to those objectives. Also, thoseInference rules where the strength of the weights or the size of theother numerical values in the rule are adjusted to optimize the observedcorrelation. May also be referred to as verified.

Modeling: Using either the verified or tuned Inference rules or theemployee and team performance metrics as inputs to a business model inorder to calculate a modeled result corresponding to the desiredobjectives. May also be referred to as forecasting.

Human-generated Hypothesis: Inference rules guessed by ‘common-sense’ orexpert opinion, possibly followed by a tuning step using comparison withhistorical data. May also be referred to as expert-generated hypothesisor expert-generated relationship.

Machine-generated Inference rule: Inference rules generated at least inpart by an algorithmic procedure, and then tuned to validate andoptimize the observed correlation to objectives. May also be referred toas machine-generated hypothesis or machine-generated relationship.

Measuring User Performance Through the Observation Platform

Embodiments of the present technology are for using structuredcommunications to measure and quantify performance metrics such as, butnot limited to: personal performance, performance within groups,performance within an enterprise environment, relative ranking againstothers in the same or similar environment, relative ranking againstothers with the same or similar roles and responsibilities, assessmentand quantification of sociability skills and benchmarking of this set ofmetrics for use across similar environments, geographies or roles. Thedata is gathered using an observation platform that may involve a numberof users, devices, other computer systems and external IoT devices whichprovides structured and disciplined communications and captures dataregarding the communications and devices in the Primary Statistics.

Employee data is gathered by the observation platform and includes datafrom five general categories of input information: Employeeconversations, queries, responses, intonation, voice cadence, geographiclocations and motions; Employee interactions with external systems anddatabases connected to the observation platform; Employee interactionswith IoT devices connected to the observation platform; Subjective datagathered within the observation platform or reported to the observationplatform by external systems; and Subjective data manually entered intothe observation platform or reported to the observation platform byother employees, visitors, customers or managers

A performance metric may also be a metric, a key performance indicator(KPI) or a business metric. A metric or performance metric as referredto herein may be any type of data associated with or derived fromcommunication between users, including the location of thecommunications device, or the words spoken and the contextual state atthe time of a particular communication event. Additional information anddata is generated and collected from proximity information betweendevices and proximity information with stationary or visitor beacons orradio signals. In one embodiment, the computer system is able togenerate a visual representation of metrics. For example, the visualrepresentation may be a map of the geographic location of the users inan environment or may be a visual indication of the availability statusof a user.

In another example, the visual representation may be textual informationsuch as the number of communications sent by a user or the length oftime it took for a user to respond to a communication. The performancemetrics may be sent in near-real-time as an audio message or displayedto a manager or other user for use in making decisions and directingactions. The performance metrics may be used by the manager to optimizecustomer service in a retail setting by taking actions such asreprimanding or rewarding an associate, combining groups or teams ofassociates with differing and necessary behaviors, skills or talents asrecorded by the observation platform, or being informed that noassociates are located near a group of customers or in a specificlocation zone. Performance metrics may also generate real-time alertsand alarms or notifications that action, intervention or coordination isneeded. These alerts and alarms are also routed to the most appropriateusers based on context, policy and inference.

An employee's past work experience alone is not the leading indicator ofwhether or not the employee is influential or a “go-to” person in theparticular retail setting. To achieve this informal status, the employeenot only needs to be viewed as having the knowledge that others need,but they also need to have other factors such as being approachable,being positive in attitude and actions, and/or socially available andresponsive for customers and employees to engage with them. These otherfactors may be described as sociability factors, sociability potential,or social skills.

Managers in the retail setting may be able to determine or measure theemployee's experience through their length of service or subjectexpertise, but when it comes to an objective measurement ofresponsiveness, helpfulness, being at the right place at the right time,effectiveness in current role, competence with external system,sociability factors or social skills, managers have few tools with whichto measure, quantify, evaluate and compare an employee's effectivenessother than subjective anecdotal or gut feelings. Consequently, whenmanagers are transferred or quit, this learned anecdotal knowledge oftenleaves with them and an incoming manager has to learn the behavioralpatterns from scratch. It is also challenging to measure performancefactors in prospective employees during the new hire process andprobationary period.

The present technology operates by using an observation platform,devices, IoT devices, external systems and structured communicationsfrom which to gather or collect primary statistics that may be used togenerate secondary statistics or higher-order statistics. The primarystatistics, secondary and higher-order statistics are utilized as a partof an assessment of the performance and behaviors of a user in theobservation platform.

The assessment may be used by the enterprise, a manager, others, orcomputer systems to make decisions for promotions, demotions, lateraltransfers, temporary assignments, interventions, leveraging talents,sharing skills and combining individuals into teams, measuringassimilation rates for new employees, or for the obtaining of othergoals. The present technology may be to run continuous assessments ofpeople skills and productivity metrics, including the dependency oncontext, and then to use the assessments to guide planning, discovercauses, provide training, and make the management task more effective byhaving objective data. The present technology includes the concept ofquantifiable ‘sociability scoring,’ and the profit-making potential ofemploying measured characteristics for the training, deployment,alignment, assessment, assimilation and management of store personal. Itshould be appreciated that the present technology may extend to otherenvironments besides a retail setting wherever it is useful to assesshuman performance and organize humans into groups depending on theirsocial skills.

The present technology uses the observation platform to intercept acommunication or instruction from a first device at a computer systemand relay the communication or instruction to a second device or secondcomputer system where the first computer system determines the relayeddestination by deriving context information from the communicationand/or stored data relevant to the communication. The first computersystem measures or collects Primary Statistics related to the devices,the communication and any external computer systems or IoT devices. Theprimary statistics can be then used to generate secondary statistics togenerate or create an assessment of a user associated with the device. Aspecific user may be associated with a device based on matchingcharacteristics of the user; as determined by comparing currentcharacteristics against prior primary and secondary statistics to derivethe identity of the user. The user may more directly identify themselvesto the system through voice printing (identification of the user by thesounds of their voice), by saying a key phrase or saying their name oran ID number.

The primary, secondary and higher-order statistics resulting inperformance assessments are the quantified performance score of theuser. The assessment is then made available to a manager, others or acomputer system for use in aligning performance with the company'sgoals. For example, the manager may create a work schedule of employeesusing the assessment to ensure that a “go-to” person is scheduled foreach shift. Thus the present technology may be used to measure orquantify high-performance or low-performance skills of employees, thetypes of skills and talents the employees demonstrates, areas needingimprovement and areas where that employee might be helpful to others.The subjective and quantitative employee performance information canthen be made available for managers, others or external computer systemsto use in making staffing, team selection, and role decisions.

Data Collection, Categorization and Generation of User PerformanceMetrics

The observation platform collects statistics for all users and devicesconnected through the system or connected to the system. Aggregation andmanipulation of the primary statistics into secondary statistics andpotentially into Higher-order Statistics results in many categories ofstatistics that indicate the performance of an employee while in theenvironment of the observation platform.

Two broad categories of data are collected within the observationplatform environment: objective data, and subjective data. Algorithmswithin the computer system attached to the observation platform thencombine the data using policy and weighting factors provided by theenterprise or the defaulted to the observation platform system. Theoutput of the algorithm becomes a component of the employee score thatis either explicitly displayed or used internally for the computation ofa Higher-order statistic that indicates a component of the employeeperformance score that is explicitly displayed.

In one embodiment, objective and subjective data may be combined toyield a performance score. For example, a manager may highly valuetime-in-zone, an objective primary statistic, with a weighting factorthat is important to his business. The weighting factor is a subjectivedata element that is combined by the algorithm and policy within theobservation platform which yields a performance score element for theuser.

As one case, performance metrics can use location information as onePrimary Statistic combined with other contextual information to producesecondary and higher-order statistics indicating the personalproductivity metrics for the user.

In one embodiment, quantifying the percent of time a device or userspends in an assigned zone, the motion within that zone, the movementspeed within the zone, the comparison of motion in the zone to outsideof the zone or with other specified zones are combined to indicateperformance factors such as energy, attentiveness and urgency. Bymeasuring the relative time spent in other zones, the algorithm candetermine the degree of task focus and attention to visitors orshoppers. By measuring the user communication patterns within andoutside of the zone of interest, the algorithm can determine suchperformance factors as focus, helpfulness, inquisitiveness, depth ofknowledge for the zone contents or function, and the willingness tofollow instructions.

In one embodiment, counting the number of times a user (User A) requeststhe location of another user (User B) as a primary statistic, can beused to determine secondary or higher-order statistics that score howmuch a person is relied on in the enterprise and how often people wantto find them (User B) or how well a manager is keeping up withsituational awareness by checking the locations of employees (User A).

In one embodiment, counting the number of times a user (User A) requests“Who is near” a specific location to hear a list of employees and theirstatus within that location, results in a primary statistic that can beused to determine secondary or higher-order statistics that score howmuch attention that user (User A) is paying to the situational awarenessof the store or enterprise.

As one case, performance metrics can be derived from request andresponse data as one Primary Statistic combined with other contextualinformation to produce secondary and higher-order statistics indicatingthe personal productivity metrics for the user. The request and responsefunction includes all cases where the system; an external device (e.g.,IoT or a smartphone); a policy; a message distributed to individuals,groups, districts or regions; or the context of the user creates theneed for an action requiring a response from one or a plethora of users.

In one embodiment, using and measuring request-and-response functions,with or without location data, as a primary statistics can be used toderive secondary and higher-order statistics that determine performancefactors such as: response speeds, personal urgency, urgency to supportothers, team commitment, and personal energy levels. By comparing therequest-and-response behaviors day-to-day, trends can be uncoveredindicating employee motivation and employee reliability on a long-termor a daily basis.

In one embodiment, using and measuring request-and-response functionsfor expert groups, with or without location data, as a primarystatistics can be used to derive secondary and higher-order statisticsthat determine if new employees are curious about products, services orprocesses and if existing employees are willing to share knowledge andtheir depth of knowledge by subject. The algorithm thus determinesperformance factors such as: employee inquisitiveness, depth of subjectknowledge, willingness to share knowledge and speed of integration intothe enterprise.

In one embodiment, combining verbal question-and-answer functions, withlocation data can be used by the algorithm to determine performancefactors such as: product knowledge, team commitment, inquisitiveness,aptitude for products or functions within the zone, and teamhelpfulness.

TABLE 1 Request and Response Examples Secondary and Higher- orderStatistics (Performance Primary Function Primary StatisticsMeasurements) Expert Group - Count of questions asked byInquisitiveness, assimilation Asking Questions user, times, locationswhere status and rate, knowledge about Processes asked, locations wheretopics, depth of process answered, time to answer, knowledge,willingness to length of answer, number of share knowledge, userslistening to answer, consistency of actions number of users NOTlistening to answer, keywords in question, keywords in answer, number ofquestions not listened to, requests heard and NOT responded to. ExpertGroup -- Count of questions asked by Inquisitiveness, assimilationAsking Questions user, times, locations where status and rate, knowledgeabout Products asked, locations where topics, depth of product answered,time to answer, knowledge, willingness to length of answer, number ofshare knowledge, users listening to answer, consistency of actionsnumber of users NOT listening to answer, keywords in question, keywordsin answer, number of questions not listened to, requests heard and NOTresponded to. Zone-coverage alerts - Measure reaction time to Teamsupport, customer Receiving alerts respond to the request, numberaffinity, understanding of that a zone is not of requests responded to,time role, job urgency, staffed or is to arrive at the coverageenvironmental awareness. overstaffed location, locations where timespent, speed traveled, path traversed, requests heard and NOT respondedto. Kiosk alerts - Measure reaction time to Urgency to support shoppersReceiving alerts that respond to the request, time to or visitors,helpfulness, depth a visitor is engaged arrive at the correct location,of knowledge regarding kiosk with a kiosk and may speed traveled, pathtraversed, products or information need attention speed to arrive inproximity of presented, awareness of visitor or shopper, length ofpriorities, customer time spent in location or in the satisfactionscores. proximity of the visitor, visitor feedback from external devicesor systems used by visitors or customers, customer feedback rating fromanother user of the system, requests heard and NOT responded to,relationship of user role to the kiosk function. “Soft button” alerts -Measure reaction time to Urgency to support shoppers receiving alertsthat a respond to the request, time to or visitors, helpfulness, depthvisitor has pressed a arrive at the correct location, of knowledgeregarding button or has verbally speed traveled, path traversed, buttonlocation products, requested help or speed to arrive in proximity ofawareness of priorities, information and they visitor or shopper, lengthof customer satisfaction scores are expecting some time spent inlocation or in the action proximity of the visitor, visitor feedbackfrom external devices or systems used by visitors or customers, customerfeedback rating from another user of the system, requests heard and NOTresponded to, relationship of user role to the kiosk function.Buy-on-line, fulfill-in- Measure reaction time to Urgency in inventorystore requests - respond to the request, time to verification, inventoryalerts or requests arrive at the inventory location, location knowledge,desire to from omni-channel speed traveled, path traversed, assist, teamcooperation and sources that speed to verify inventory, supportinventory needs to be speed to move inventory to verified or heldholding location, voice inflections Pick-up requests - Measure reactiontime to Urgency to support shoppers requests for products respond to therequest, time to or visitors, helpfulness, to be delivered to arrive atthe inventory location, desire to assist, customer specific locationsspeed traveled, path traversed, satisfaction scores. speed to verifyinventory, locations visited, voice inflections, visitor feedback fromexternal devices or systems used by visitors or customers Inventoryverification Measure reaction time to Urgency in inventory requests -requests respond to the request, time to verification, inventory fromsystems or arrive at the inventory location, location knowledge, desireto other stores to speed traveled, path traversed, assist, teamcooperation and validate inventory speed to verify inventory, supportstatus locations visited, voice inflections, speech cadence Respondingto Measure times through zone Task orientation, willingness locationbased tasks with and without action, speed to take on new tasks, task -tasks that are through zone, adjacency to efficiency, quality of heardwhen a user visitors or shoppers while workmanship, affinity to movesinto a specific passing through zone, time to types of tasks zone closetask after entering zone, voice inflections, speech cadence, subjectfeedback on quality and completeness of task within zone, task requestsheard and NOT responded to.

In one embodiment, combining proximity information by detecting shopperor visitor devices using beacons, near-field or other technologies suchas RFID, with location data of the device can be used by the algorithmto determine performance factors such as: shopper or visitorhelpfulness, efficiency in helping shoppers or visitors, desire toengage shoppers or visitors, effectiveness in engaging shoppers orvisitors and effectiveness in supporting shoppers or visitors byhigh-value items or functions within the zone.

In one embodiment, combining proximity information by detecting otherobservation platform user devices, with location data can be used by thealgorithm to determine performance factors such: as team leadership,training or teaching efforts, time wasting or cooperativeness.

In one embodiment, combining point of sale (POS) information, withlocation data can be used by the algorithm to determine performancefactors such as: selling effectiveness within assigned zone, sellingeffectiveness in other zones, attention to basket size, conversion ratescores, attention to product margins.

In one embodiment, combining shopper or visitor feedback informationgathered by shopper or visitor applications that are able to detect theobservation platform user, with location data can be used by thealgorithm to determine performance factors such as: customersatisfaction scores by zone covered, customer satisfaction vs. timespent with the customer, and other performance metrics as derived fromsurvey tool applications used by the shopper or visitor

In a different case, performance metrics can use external systeminstructions as one primary statistic combined with other contextualinformation to produce secondary and higher-order statistics indicatingthe personal productivity metrics for the user.

In one embodiment, combining shopper or visitor encounter informationgathered by detecting shopper or visitor applications using BLE,Bluetooth, Cellular or WiFi signal detection, with external systeminstructions can be used by the algorithm to determine performancefactors such as: social interaction scores, helpfulness scores, shopperor visitor attentiveness scores, and urgency to assist scores.

In one embodiment, combining task requests originating from externalsystem instructions with other context information can be used by thealgorithm to determine performance factors such as: reaction time topick up assigned or requested tasks, reaction time to verify on-shelf orbackroom inventory, task compliance performance, task urgencycompliance, and competitiveness scores.

In one embodiment, combining time-clock information from externalsystems with user behavior within the observation platform environmentcan be used by the algorithm to determine performance factors such as:ability to plan actions, ability to complete tasks in a timely manner,ability to complete tasks within daily deadlines, and ability to keep toschedules.

In one embodiment, combining video camera information from externalsystems with user behavior within the observation platform environmentcan be used by the algorithm to determine performance factors such as:ability to address customers or visitors appropriately, ability torecognize poor coverage zones or assist groups of customers or visitors,urgency to address loss prevention alerts, ability to assist and supportdiverse cultures of customers or visitors.

In a different set of cases, performance metrics can use informationmanually entered into either the observation platform or an externalsystem as one Primary Statistic combined with other contextualinformation to produce Secondary and Higher-order statistics indicatingthe personal productivity metrics for the user.

In one embodiment, combining external, manually entered information withuser behavior and context within the observation platform environmentcan be used by the algorithm to determine performance factors such as:absorption and application of course work completed, absorption andapplication of video, audio or other training methodologies, absorptionand application of brand-oriented messages and information provided bythe enterprise, and correlation of course grades to the application ofthe material in the observation environment.

In one embodiment, combining external, manually entered information fromcustomers or visitors that is requested via an application running on adevice carried by the customer or visitor, with user behavior andcontext within the observation platform environment can be used by thealgorithm to determine performance factors such as: customersatisfaction scores, ability to pay attention and focus, ability toanswer questions, demeanor, helpfulness, alertness, depth of knowledge,and breadth of knowledge.

In one embodiment, combining external, manually entered information fromother users of the system or peers, which is requested via anapplication running on a device carried by the user or from a terminalconnected to the observation platform, with user behavior and usercontext within the observation platform environment can be used by thealgorithm to determine performance factors such as: team player scores,helpfulness scores, knowledge sharing scores, customer satisfactionscores, demeanor toward co-workers, alertness, depth of knowledge, andbreadth of knowledge.

In one embodiment, combining external, manually entered information fromother users of the system or peers, which is requested via a scheduledprocess or adjusted by enterprise policy controlling the scheduledprocess within the observation platform, with user behavior and usercontext within the observation platform environment can be used by thealgorithm to determine performance factors such as: progress towardgoals, team player scores, helpfulness scores, knowledge sharing scores,customer satisfaction scores, demeanor toward co-workers, alertness,depth of knowledge, and breadth of knowledge.

In one embodiment, combining external, manually entered information fromthe primary user with that same user's behavioral data and contextwithin the observation platform environment can be used by the algorithmto determine performance factors such as: progress toward goals, newpotential goals, attitude towards tasks, goals, career and company; teamplayer scores; demeanor toward co-workers or managers; desire forimprovement; and willingness to accept feedback. Additionally, users canoffer commentary, explanations or rebuttals to the objective performancescores in the system.

In one embodiment, combining external, manually and automaticallyentered information regarding length of employment and past roles andresponsibilities can be used by the algorithm to determine performancefactors such as: progress toward goals, new potential goals, attitudetowards tasks, goals, career and company; team player scores; demeanortoward co-workers or managers; desire for improvement; willingness toaccept feedback; and other career determining or limiting factors.

In one embodiment, combining information from point-of-sale (POS)systems with the primary and secondary statistics of the user,especially proximity information gathered by beacons or BLE connections,can be used by the algorithm to determine performance factors such as:sales effectivity/efficiency (e.g., sales dollars per minute spent withshoppers), ability to upsell and influence add-on purchases, ability tomove the shopper to different departments and increase basket size,ability to create conversions (sales) with shorter customer encounters.

Example algorithms for producing quantified performance measurements andmetrics are shown in the table below. These algorithms represent one ofmany possible calculations for each performance metric. The algorithmexamples use the coefficient “C” as a normalizing and weighting factorfor each performance category. The other capital letter coefficients areused within the algorithm to weight each performance factor. All ofthese coefficients many be adjusted based on the business requirementsof each environment.

Primary Function Typical Algorithm Shown as an Example Result ExpertInquisitiveness: C[+M(number of questions A numerical value Group -asked/user-hr) + N(number of questions listened normalized in the Askingto/user-hr) + P(time spent listening to range of typically 0- Questionsanswers/user-hr) − Q(number of questions NOT 100 (by the constant aboutlistened to/user-hr)] value of C) Processes Assimilation Status:C′[+M(number of indicating the [Process questions asked/user-hr) +N(number of performance of the questions questions listenedto/user-hr) + A(the number of user for the are identified questionsanswered/ user-hr) − T(average category. by either a response time toanswer a question) − Q(number For “Knowledge specific of questions NOTanswered/user-hr)] Topics” the result is command or Assimilation Rate:(the linear slope of the list of keyword a recognized above equationcomputed over an interval of topics in ranked set of time) order offrequency keywords] Depth of Process Knowledge: C″[−M(number ofresponding. of questions asked/user-hr) + A(the number of questionsanswered/user-hr) + W(the number of users who listen to the full answer)− X(the number of users who do NOT listen to full answer) − T(averageresponse time to answer a question) − Q(number of questions NOTanswered/user-hr)] Willingness to Share Knowledge: C″′[+A(the percentageof questions answered) − T(average response time to answer a question) −Q(number of questions NOT answered/user-hr)] Consistency of Actions:Consistency is computed as the standard deviation (or other statisticalmodel) for the set of characteristics defined above. Knowledge Topics:[keyword]*C″′[M(number of questions asked/user-hr) + N(number ofquestions listened to/user-hr) + A(the number of questionsanswered/user-hr) − T(average response time to answer a question) −Q(number of questions NOT answered/user-hr)] Expert Inquisitiveness:C[+M(number of questions A numerical value Group -- asked/user-hr) +N(number of questions listened normalized in the Asking to/user-hr) +P(time spent listening to range of typically 0- Questionsanswers/user-hr) − Q(number of questions NOT 100 (by the constant aboutlistened to/user-hr)] value of C) Products Assimilation Status:C′[+M(number of indicating the Product questions asked/user-hr) +N(number of performance of the questions questions listenedto/user-hr) + A(the number of user for the are identified questionsanswered/ user-hr) − T(average category. by either a response time toanswer a question) − Q(number For “Knowledge specific of questions NOTanswered/user-hr)] Topics” the result is command or Assimilation Rate:(the linear slope of the list of keyword a recognized above equationcomputed over an interval of topics in ranked set of time) order offrequency keywords] Depth of Process Knowledge: C″[−M(number ofresponding. of questions asked/user-hr) + N(number of questions listenedto/user-hr) + A(the number of questions answered/user-hr) + W(the numberof users who listen to the full answer) − X(the number of users who doNOT listen to full answer) − T(average response time to answer aquestion) − Q(number of questions NOT answered/user-hr)] Willingness toShare Knowledge: C″′[+A(the percentage of questions answered) −T(average response time to answer a question) − Q(number of questionsNOT answered/user-hr)] Consistency of Actions: Consistency is computedas the standard deviation (or other statistical model) for the set ofcharacteristics defined above. Knowledge Topics: [keyword]*C″′[M(numberof questions asked/user-hr) + N(number of questions listenedto/user-hr) + A(the number of questions answered/user-hr) − T(averageresponse time to answer a question) − Q(number of questions NOTanswered/user-hr)] Zone- Team Support: C[T(average proximity to other Anumerical value coverage users) + R(reaction time to respond to anormalized in the alerts - request) − S(average time to arrive at therange of typically 0- Receiving coverage location) + L(speed 100 (by theconstant alerts that a traveled) + N(number of requests responded valueof C) zone is not to/user-hr) − Q(number of requests NOT indicating thestaffed or is responded to/user-hr)] performance of the overstaffedCustomer Affinity: C′[+R(reaction time to user for the respond to arequest) − S(average time to arrive category. at the coveragelocation) + L(speed traveled) + N(number of requests respondedto/user-hr) − Q(number of requests NOT responded to/user-hr)]Understanding of Role: C″[A(time spent in target location) + R(reactiontime to respond to a request) − S(average time to arrive at the coveragelocation) + L(speed traveled) + N(number of requests respondedto/user-hr) − Q(number of requests NOT responded to/user-hr)] JobUrgency: C″′[A(time spent in target location) + R(reaction time torespond to a request) − S(average time to arrive at the coveragelocation) + L(speed traveled)] Environmental Awareness: C″″[A(time spentin target location) + B(number of other locations visited/user-hr) −C(number of locations not visited/user-hr) − P(the path length traveledvs. the average path length for all users between the starting point andthe ending point)] Kiosk alerts - Urgency to Support Visitors:C[+R(reaction A numerical value Receiving time to respond to a kioskrequest) − S(average normalized in the alerts that a time to arrive atthe coverage location) + L(speed range of typically 0- visitor istraveled) + N(number of requests responded 100 (by the constant engagedto/user-hr) − Q(number of requests NOT value of C) with a kioskresponded to/user-hr)] indicating the and may Helpfulness:C′[+R(reaction time to respond to performance of the need a kioskrequest) − S(average time to arrive at the user for the attentioncoverage location) + L(speed category. traveled) + N(number of requestsresponded to/user-hr) − Q(number of requests NOT responded to)] + T(timespent at the kiosk location) + V(time spent in the proximity of thevisitor)] Depth of Knowledge Regarding Kiosk Products: C″[+R(reactiontime to respond to a kiosk request) + A(the number of questions withrelevant keywords answered/user-hr) + W(the number of users who listento the full answer) − X(the number of users who do NOT listen to fullanswer) − T(average response time to answer a question with relevantkeywords) − Q(number of questions NOT answered/user-hr) with relevantkeywords] Awareness of Priorities: C″′[+R(reaction time to respond to akiosk request) − S(average time to arrive at the coverage location) +L(speed traveled) + N(number of requests responded to/user-hr) −Q(number of requests NOT responded to)] + E(relationship of role tokiosk function)] Customer Satisfaction Scores: C″″[+R(reaction time torespond to a kiosk request) − S(average time to arrive at the coveragelocation) + L(speed traveled) + N(number of requests respondedto/user-hr) − Q(number of requests NOT responded to)] + F(customerfeedback scores collected from kiosk or applications related to kiosk) +G(customer feedback rating from another user of the system)] “Softbutton” Urgency to Support Visitors: C[+R(reaction A numerical valuealerts - time to respond to a kiosk request) − S(average normalized inthe receiving time to arrive at the coverage location) + L(speed rangeof typically 0- alerts that a traveled) + N(number of requests responded100 (by the constant visitor has to/user-hr) − Q(number of requests NOTvalue of C) pressed a responded to/user-hr)] indicating the button andHelpfulness: C′[+R(reaction time to respond to performance of theexpecting a kiosk request) − S(average time to arrive at the user forthe some action coverage location) + L(speed category. traveled) +N(number of requests responded to/user-hr) − Q(number of requests NOTresponded to/user-hr)] + T(time spent in the proximity of the softbutton location) + V(time spent in the proximity of the visitor)] Depthof Knowledge Regarding Kiosk Products: C″[+R(reaction time to respond toa soft button request) + A(the number of questions with relevantkeywords answered/user-hr) + W(the number of users who listen to thefull answer) − X(the number of users who do NOT listen to full answer) −T(average response time to answer a question with relevant keywords) −Q(number of questions NOT answered/user-hr) with relevant keywords]Awareness of Priorities: C″′[+R(reaction time to respond to a kioskrequest) − S(average time to arrive at the coverage location) + L(speedtraveled) + N(number of requests responded to/user-hr) − Q(number ofrequests NOT responded to)] +E (relationship of role to soft buttonrequest type)] Customer Satisfaction Scores: C″″+R(reaction time torespond to a kiosk request) − S(average time to arrive within proximityof the soft button) + L(speed traveled) + N(number of requests respondedto/user-hr) − Q(number of requests NOT responded to)] + F(customerfeedback scores collected from soft button or applications related tosoft button) + G(customer feedback rating from another user of thesystem)] + V(positive voice inflection scores for communications overthe past T user-hrs) Buy-on-line, Urgency in Inventory Verification: Anumerical value pick-up-in- C[+R(reaction time to respond to a BOPISnormalized in the store request) − S(average time to arrive at the rangeof typically 0- (BOPIS) inventory location) + L(speed 100 (by theconstant requests - traveled) + N(number of BOPIS requests value of C)alerts or responded to/user-hr) − Q(number of BOPIS indicating therequests requests NOT responded to/user-hr)] performance of the fromomni- Inventory Location Knowledge: user for the channel C′[+R(reactiontime to respond to a BOPIS category. sources that request) − S(averagetime to arrive at the inventory inventory location) + L(speed traveled)− P(the needs to be path length traveled vs. the average path lengthverified or for all users between the starting point and the held endingpoint)] Desire to Assist: C″[+R(reaction time to respond to a BOPISrequest) − S(average time to arrive at the product location) + L(speedtraveled) + N(number of requests responded to/user-hr) − Q(number ofrequests NOT responded to/user-hr)] + E(relationship of role to BOPISrequest type)] Team Cooperation: C″′[+R(reaction time to respond to aBOPIS request) − S(average time to arrive at the product location) +L(speed traveled) + N(number of requests responded to/user-hr) −Q(number of requests NOT responded to/user-hr)] + V(positive voiceinflection scores for communications over the past T user-hrs) Pick-upor Urgency to Support Visitors: C[+R(reaction A numerical value CarryOut time to respond to a request) − S(average time to normalized in therequests - arrive at the required location) + L(speed range of typically0- requests for traveled) + N(number of requests responded 100 (by theconstant products to to/user-hr) − Q(number of requests NOT value of C)be delivered responded to/user-hr)] indicating the to specificHelpfulness: C′[+R(reaction time to respond to performance of thelocations a request) − S(average time to arrive at the user for therequired location) + L(speed traveled) + N(number category. of requestsresponded to/user-hr) − Q(number of requests NOT respondedto/user-hr)] + T(time spent from arriving at the required location toindicating the task is complete) + V(time spent in the proximity of thevisitor)] Desire to Assist: C″[+R(reaction time to respond to a request)− S(average time to arrive at the required location) + L(speedtraveled) + N(number of requests responded to/user-hr) − Q(number ofrequests NOT responded to/user-hr)] + E(relationship of role to Pick-upor Carry Out request type)] Customer Satisfaction Scores:C″′[+R(reaction time to respond to a request) − S(average time to arrivewithin proximity of the request) + L(speed traveled) + N(number ofrequests responded to/user-hr) − Q(number of requests NOT respondedto/user- hr)] + F(customer feedback scores collected from an applicationrunning in a visitor device) + G(customer feedback rating from anotheruser of the system)] + V(positive voice inflection scores forcommunications over the past T user-hrs) Inventory Urgency in InventoryVerification: A numerical value verification C[+R(reaction time torespond to a verification normalized in the requests - request) −S(average time to arrive at the range of 0-100 (by requests inventorylocation) + L(speed the constant value from traveled) + N(number ofverification requests of C) indicating the systems or respondedto/user-hr) − Q(number of verification performance of the other storesrequests NOT responded to/user-hr)] user for the to validate InventoryLocation Knowledge: category. inventory C′[+R(reaction time to respondto a verification status request) − S(average time to arrive at theinventory location) + L(speed traveled) − P(the path length traveled vs.the average path length for all users between the starting point and theending point)] Desire to Assist: C″[+R(reaction time to respond to averification request) − S(average time to arrive at the requiredlocation) + L(speed traveled) + N(number of verification requestsresponded to/user-hr) − Q(number of verification requests NOT respondedto/user- hr)] + E(relationship of role to inventory verification requesttype)] Team Cooperation: C″′[+R(reaction time to respond to an inventoryverification request) − S(average time to arrive at the productlocation) + L(speed traveled) + N(number of inventory verificationrequests responded to/user-hr) − Q(number of inventory verificationrequest NOT responded to/user-hr)] + V(positive voice inflection scoresfor communications over the past T user-hrs) Responding TaskOrientation: C[+D((number of location- A numerical value to locationtasks accepted/user-hr)/(number of location- normalized in the basedtasks - tasks heard/user-hr)) − T(time to indicate task range oftypically 0- tasks that completion)] 100 (by the constant are heardWillingness to Take on New Tasks: value of C) when a user C′[+D((numberof location-tasks accepted/user- indicating the moves into a hr)/(numberof location-tasks heard/user- performance of the specific zone hr)) +N(number of different locations for user for the accepted tasks) −M(number of different locations category. for tasks NOT accepted) For“Affinity to Task Efficiency: C″[+D((number of location- Types of Tasks”the tasks accepted/user-hr)/(number of location- result is list of tasktasks heard/user-hr)) − H(time from task locations in ranked acceptanceto task complete)] order of frequency Quality of Workmanship:C″′[+D((number of of acceptance in a location-tasks accepted/user-hr) −H(time from given location. task acceptance to task complete) +B(external rating of workmanship from other users or managers)] Affinityto Types of Tasks: C″″[+D((number of location-tasks accepted/user-hr foreach location)]Employee Performance Library

The data gathered and processed within the observation platform isunique in that enterprise or store employees have traditionally beenleft out of the Information Technology (IT) infrastructure unless theywere at a terminal or using a hand-held device. The observation platformprovides continual geographic location tracking and monitoring of allemployee interactions with other employees or connected systems thusallowing the accumulation and analysis of a new set of key performanceindicators (KPIs) for hourly workers.

These new KPIs are a combination of objective and subjective metricsregarding how an employee or store associate performs tasks andinteracts with other peers and managers. The observation platformapplies inference rules, policy and statistical approaches to the uniqueinformation gathered and creates usable, definable, benchmarkable andtransferrable employee performance metrics. These metrics may then betabulated and recorded as a part of a new kind of employee performancerecord.

Since the employee performance record is based on objective statisticsagainst a history of roles and responsibilities, these KPIs may be usedto indicate the performance of an employee in a new role, department,division, or in a new enterprise entirely.

Because the KPIs are useful to other organizations, divisions,departments or roles; these individual performance indicators can bemade available to others in the enterprise or in other enterprises muchlike an “Equifax” credit score is available to banks or lenders.

The unique information gathered by the observation platform thereforeallows the creation of new kind of employee performance database whereobjective Primary Statistical data is processed into SecondaryStatistics or Higher-order Statistics indicative of an employee's pastand predicted performance based on role, function or enterpriseoperations.

Some basic aspects of the employee scoring, rating and ranking systeminclude, but are not limited to:

The system creating a personal performance library that may be sharedwith the employee, employer, the observation platform owners, or otherdesignated organizations who may then share the information with others.

Employees being granted access to their performance data according tothe enterprise policy for sharing such data. The enterprise isencouraged to share the employee information external to the enterpriseand encourage other enterprises to share employee information, so they,in turn, can pre-view candidate performance information before makinghiring decisions.

The employee is given tools for managing the distribution of theirperformance information according to the policy of the enterprisegathering the information. The tools allow accessing, reviewing,commenting and disputing the content stored in the library. Theobservation platform owner may also retain rights to the employeeinformation.

The employee information library is organized with “public” and“private” areas. The enterprise, employee, or observation platform ownercan, according to privacy policy, move/copy data between the private,semi-public and public areas.

Employee performance information is used for personal growth in currentpositions and as guide for establishing capabilities based on objectivedata of past performance for use in seeking more relevant or valuableemployment in different roles, departments, divisions or with entirelydifferent enterprises.

The performance information helps employees identify core strengths androles where they will be most successful.

Performance information in the library is used by current employers orfuture employers as determined by the privacy policy agreed between theemployee, enterprise and observation platform owner.

The current employer uses the system to measure employee assimilation orprogram alerts that an intervention may be helpful.

The name of the enterprise or department associated with the source ofthe data is made anonymous by the policies driving the decisions in thesystem.

The system will update the employee data in near-real-time providing acurrent employee performance assessment report on a daily, weekly,monthly or other basis.

The employee performance library will utilize procedures to alert theemployee as changes are made to any performance category or if newperformance categories are added.

Data Archiving and Security

Once the data is collected, it is archived for analysis by the inferenceengines; Date is parsed and may be stored in multiple tables; data isencrypted; authentication, authorization, and accounting with securetransport (AAAS) is assured; and processed data is validated againstnorms to eliminate errored data.

Data Manipulation and Inference

Establishment of norms by role, group, zone assignments and arbitraryfactors entered by managers or system defaults; Ranking of individualperformance data against norms; Weighting of data categories and normsfor particular roles, groups, zones and arbitrary factors;Identification of outlier behaviors (performance significantly above orbelow norms); and translation function for weighting the data againstthe criteria for different store categories or operational models.

Some Examples of Data Elements in Employee Library

Subjective data includes: survey feedback from peers and managers;survey feedback from visitors (shoppers); disengagement feedback fromvisitors (shoppers); tone-of-voice quantification and calibrationagainst individual norms; voice inflection quantification andcalibration against individual norms.

Secondary and higher-order statistics indicative of employee performanceincludes: responsiveness to tasks; responsiveness to questions; abilityto ask questions (curiosity); communication balance; communicationbreadth (laterally across the organization and up/down the managerialchange); keeping up with messages and announcements; and prioritizingtasks.

Benchmarking data includes: establishing performance criteria for roles,locations, chains, stores, departments, and enterprises; calculatingmeans and standard-deviations for the criteria amenable to statisticalanalysis; establishing a normalized benchmark for roles, location,chains, stores, departments and enterprises; and dynamically updatingthe benchmarks with new information.

Relative data includes: ranking within peer group; ranking within singlestore (office); ranking within district of stores (district orenterprise division); ranking within region of stores (geographicalenterprise divisions); ranking within chain (enterprise); ranking withinrole (for the above); ranking for zone coverage; and ranking withinindustry category.

What is claimed is:
 1. A method of using an observation platform todetermine relative responsiveness of enterprise employees to requests byexternal systems, the method comprising: monitoring, by a computersystem of the observation platform, responses and actions of users torequests from external systems from a plurality of communication deviceswhich have been routed through a radio access point associated with thecomputer system, wherein each of the communication devices is associatedwith a user who is an employee in an enterprise; extracting, by thecomputer system, information from the communications includingidentities of the users of the communications devices; measuring, by thecomputer system, performance of a plurality of the users based onaspects of the extracted information which are related to the relativeresponsiveness to requests from the external systems, wherein therequests are posed by the external systems and responded to via thecommunications devices; and assigning, by the computer system, anumerical ranking of the measured performance by each of the pluralityof measured users, with respect to others of the plurality of measuredusers, at responding to the requests.
 2. The method as recited in claim1, wherein the extracting, by the computer system, information from thecommunications including identities of the users of the communicationsdevices comprises: using speech recognition to convert speech in thecommunications to one of text and a machine-compatible format.
 3. Themethod as recited in claim 1, wherein the measuring, by the computersystem, performance of a plurality of the users based on aspects of theextracted information which are related to the relative responsivenessto requests from the external systems comprises: measuring, by thecomputer system, performance of the plurality of the users based onthose of the aspects of the extracted information which are related toat least one of reaction times and motions of the user to a requestposed by at least one of the external systems by objectively measuringurgency to support the request for each of the measured users.
 4. Themethod as recited in claim 1, wherein the measuring, by the computersystem, performance of a plurality of the users based on aspects of theextracted information which are related to the relative responsivenessto requests from the external systems comprises: measuring, by thecomputer system, performance of the plurality of the users based onthose of the aspects of the extracted information which are related tohelpfulness to support a request posed by at least one of the externalsystems by objectively measuring a relative helpfulness metric for eachof the measured users.
 5. The method as recited in claim 1, wherein themeasuring, by the computer system, performance of a plurality of theusers based on aspects of the extracted information which are related tothe relative responsiveness to requests from the external systemscomprises: measuring, by the computer system, performance of theplurality of the users based on those of the aspects of the extractedinformation which are related to urgency of inventory verification for arequest posed by at least one of the external systems by objectivelymeasuring urgency of actions for inventory verification for each of themeasured users.
 6. The method as recited in claim 1, wherein themeasuring, by the computer system, performance of a plurality of theusers based on aspects of the extracted information which are related tothe relative responsiveness to requests from the external systemscomprises: measuring, by the computer system, performance of theplurality of the users based on those of the aspects of the extractedinformation which are related to actions and speed of response to aninventory verification request posed by at least one of the externalsystems by objectively measuring inventory location knowledge for eachof the measured users.
 7. The method as recited in claim 1, wherein themeasuring, by the computer system, performance of a plurality of theusers based on aspects of the extracted information which are related tothe relative responsiveness to requests from the external systemscomprises: measuring, by the computer system, performance of theplurality of the users based on those of the aspects of the extractedinformation which are related to responding to abuy-online-pickup-in-store (BOPIS) request posed by at least one of theexternal systems by objectively measuring a desire to assist in theBOPIS request for each of the measured users.
 8. The method as recitedin claim 1, wherein the measuring, by the computer system, performanceof a plurality of the users based on aspects of the extractedinformation which are related to the relative responsiveness to requestsfrom the external systems: measuring, by the computer system,performance of the plurality of the users based on those of the aspectsof the extracted information which are related to responding to at leastone of a pick-up request and a carry out request from at least one ofthe external systems by objectively measuring a customer satisfactionfor each of the measured users.
 9. The method as recited in claim 1,wherein the measuring, by the computer system, performance of aplurality of the users based on aspects of the extracted informationwhich are related to the relative responsiveness to requests from theexternal systems: measuring, by the computer system, performance of theplurality of the users based on aspects of the extracted informationwhich are related to responding to location based tasks by objectivelymeasuring a willingness to take on new tasks for each of the measuredusers.
 10. The method as recited in claim 1, wherein the measuring, bythe computer system, performance of a plurality of the users based onaspects of the extracted information which are related to the relativeresponsiveness and actions to requests from the external systemscomprises: measuring at least one of physical responsiveness and verbalresponsiveness to at least one of the requests.
 11. A non-transitorycomputer readable storage medium comprising instructions for causing acomputer system of an observation platform to perform method of using anobservation platform to determine relative responsiveness of enterpriseemployees to requests by external systems, the method comprising:monitoring, by a computer system of the observation platform, responsesand actions of users to requests from the external systems from aplurality of communication devices which have been routed through aradio access point associated with the computer system, wherein each ofthe communication devices is associated with a user who is an employeein an enterprise; extracting, by the computer system, information fromthe communications including identities of the users of thecommunications devices; measuring, by the computer system, performanceof a plurality of the users based on aspects of the extractedinformation which are related to the relative responsiveness to requestsfrom the external systems, wherein the requests are posed by externalsystems and responded to via the communications devices; and assigning,by the computer system, a numerical ranking of the measured performanceby each of the plurality of measured users, with respect to others ofthe plurality of measured users, at responding to the requests.
 12. Thenon-transitory computer readable storage medium of claim 11, wherein themeasuring, by the computer system, performance of a plurality of theusers based on aspects of the extracted information which are related tothe relative responsiveness to requests from the external systemscomprises: measuring, by the computer system, performance of theplurality of the users based on those of the aspects of the extractedinformation which are related to at least one of reaction times andmotions of the user to a system requests by objectively measuringurgency to support the request for each of the measured users.
 13. Anobservation platform for determining relative responsiveness ofenterprise employees to requests by external systems, the observationplatform comprising: a plurality of communication devices, wherein eachof the communication devices is associated with a user who is anemployee in an enterprise; a radio access communicatively coupled withthe plurality of communications devices; and a computer systemassociated with the radio access point and configured to: monitorcommunications from and to the plurality of communication devices andexternal systems which have been routed through the radio access point;extract information from the communications including identities of theusers of the communications devices; measure performance of a pluralityof the users based on aspects of the extracted information which arerelated to the relative responsiveness to requests from the externalsystems, wherein the requests are posed by the external systems andresponded to via the communications devices; and assign a numericalranking of the measured performance by each of the plurality of measuredusers, with respect to others of the plurality of measured users, atresponding to the requests.
 14. The observation platform of claim 13,further comprising: an external computer system, of the externalcomputer systems, coupled via a network to the computer system andconfigured to generate one of indicators and requests for one or more ofthe users of the communication devices.
 15. The observation platform ofclaim 13, wherein a portion of the extracted information comprises: textconverted, using speech recognition, from speech in the communications.16. The observation platform of claim 13, wherein the computer systemconfigured to measure performance of a plurality of the users based onaspects of the extracted information which are related to the relativeresponsiveness to requests from the external systems comprises: thecomputer system configured to measure performance of the plurality ofthe users based on those of the aspects of the extracted informationwhich are related to responsiveness to requests within the enterprisecomprises by objectively measuring responsiveness of each of themeasured users.
 17. The observation platform of claim 13, wherein thecomputer system configured to measure performance of a plurality of theusers based on aspects of the extracted information which are related tothe relative responsiveness to requests from the external systemscomprises: the computer system configured to measure the performance ofthe plurality of the users based on those of the aspects of theextracted information which are related to responsiveness to requestswithin the enterprise comprises by objectively measuring urgency ofresponse for each of the measured users.
 18. The observation platform ofclaim 13, wherein the computer system configured to measure performanceof a plurality of the users based on aspects of the extractedinformation which are related to the relative responsiveness to requestsfrom the external systems comprises: the computer system configured tomeasure performance of the plurality of the users based on those of theaspects of the extracted information which are related to responding toa buy-online-pickup-in-store (BOPIS) request posed by at least one ofthe external systems by objectively measuring a desire to assist in theBOPIS request for each of the measured users.
 19. The observationplatform of claim 13, wherein the computer system configured to measureperformance of a plurality of the users based on aspects of theextracted information which are related to the relative responsivenessto requests from the external systems comprises: the computer systemconfigured to measure performance of the plurality of the users based onthose of the aspects of the extracted information which are related toresponding to at least one of a pick-up request and a carry out requestfrom at least one of the external systems by objectively measuring acustomer satisfaction for each of the measured users by objectivelymeasuring a customer satisfaction score for each of the measured users.20. The observation platform of claim 13, wherein the computer systemconfigured to measure performance of a plurality of the users based onaspects of the extracted information which are related to the relativeresponsiveness to requests from the external systems comprises: thecomputer system, configured to measure performance of the plurality ofthe users based on those of the aspects of the extracted informationwhich are related to responding to location based tasks by objectivelymeasuring a willingness to take on new tasks for each of the measuredusers.
 21. The observation platform of claim 13, wherein the computersystem configured to measure performance of a plurality of the usersbased on aspects of the extracted information which are related to therelative responsiveness to requests from the external systems comprises:the computer system configured to measure performance of the pluralityof the users based on those of the aspects of the extracted informationwhich are related to urgency of inventory verification for a requestposed by at least one of the external systems by objectively measuringurgency of actions for inventory verification for each of the measuredusers.
 22. The observation platform of claim 13, wherein the computersystem configured to measure performance of a plurality of the usersbased on aspects of the extracted information which are related to therelative responsiveness to requests from the external systems comprises:the computer system configured to measure performance of the pluralityof the users based on those of the aspects of the extracted informationwhich are related to actions and speed of response to inventoryverification requests by objectively measuring inventory locationknowledge for each of the measured users.